Humans have opinions but, what about Machines? How can you teach machines to read people’s opinions and their preferences? And above all why does it matter? Well, these are the questions that are raised on a daily basis on the upcoming news to Machine Learning Technologies. Today Sentiment Analysis, which is a subset of Natural Language Processing (NLP), has made a completely different impression on the millennials.

Let’s dive in and understand why Sentiment Analysis matters:

Sentiment analysis is also called opinion mining which is the technique to identify and extract subjective information from text or audio. Online reviews and customer support requests are a few of the best examples of sentiment analysis. In simple words sentiment analysis determines whether subjective data is negative, positive, or neutral. However, thanks to the advancement of machine learning technologies, now brands can also use sentiment analysis for challenging use cases which are like understanding less conventional language uses, monitoring online behavior, and identifying emotions.

Sophisticated recommendation engines are used by online stores like Amazon’s which rely heavily on sentiment analysis to predict the preferences of their customers. Today, highly sophisticated technology goes above and beyond for utilizing product ratings such as learning how popular a specific product is and why.

At present brands are making use of Sentiment Analysis also to prioritize customer support tickets or to determine the most effective communication channels and preplan product improvements in the future. Altogether this value will aid in leading the brands for improving and enhancing customer experience profitability and new opportunities in business.

This vast amount of already available public information, social media, and other media platforms are helping the brands to implement sentiment analysis seamlessly and achieve greater transparency and drive citizen engagement by figuring out what and how people are responding to various matters. Reviewing sentiment analysis also enables the government and policymakers to identify widespread societal and epidemiological issues before they break out.

Here’s the best way to approach Sentiment Analysis Training

• For building a relevant sentiment analysis algorithm, analysis model developers need massive amounts of labeled data for training the model.
• They must focus on context and quality assurance while choosing a data preparation team.
• Also, they must have access to a better team that ensures improved quality and assurance that are aligned to the project goals.
• And on the other hand, the outsourced model manages the workforce and provides greater scalability and flexibility which matches crowdsourcing for teaming up with the gig workers.

About Us:

Data Labeler can be your ideal partner for data annotation and data labeling procedures. We specialize in offering convenient accurate customized and quality labeled datasets for your Machine Learning and Artificial Intelligence Initiatives.

Data Labeler possesses sophisticated workforce management software which enables you with seamless labeling and training experiences. With cost-effective solutions and being a highly efficient labeling partner, Data Labeler is your one-stop shop for all your data annotation and labeling needs. Are you still deciding on how to make the best of your AI technologies? Contact Us now.